New Dataset Tests Vision-Language Models on Cultural Understanding of Chinese Heritage Sites
Researchers introduced ChinaHeritaQA, a dataset of 2,279 images and 14,133 bilingual question-answer pairs covering UNESCO World Heritage sites in China, to evaluate how well AI vision-language models understand cultural and historical context. The dataset spans seven cognitive dimensions from basic visual recognition to architectural analysis and historical periodization. The study found that while top models perform well on visual recognition tasks, they struggle significantly with culturally grounded reasoning about heritage sites.
ChinaHeritaQA is a multimodal benchmark dataset designed to assess the cultural reasoning capabilities of vision-language models (VLMs) on Chinese UNESCO World Heritage sites. The dataset contains 2,279 in-the-wild images paired with 14,133 multiple-choice QA pairs in both Chinese and English, organized across seven cognitive dimensions ranging from basic identity recognition to complex historical periodization and architectural analysis. The dataset was developed using a UNESCO-aligned heritage ontology and underwent rigorous human annotation to ensure linguistic quality and factual accuracy. Evaluations of state-of-the-art VLMs revealed a significant gap: while top models exceeded human performance on average, they showed substantial variation across task types, excelling at visual recognition but struggling with culturally grounded reasoning. Performance also varied by dynasty and geographic region, suggesting that strong visual retrieval capabilities do not necessarily translate to cultural and historical understanding.
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- arXiv cs.CLCenter
ChinaHeritaQA: A Culturally-Grounded Visual Question Answering Dataset for World Heritage Sites in China
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